Surrogate duality for robust quasiconvex vector optimization 110 KB ( 限定公開 )
Suzuki, Satoshi Department of Mathematics, Interdisciplinary Graduate School of Science and Engineering, Shimane University
In this paper, we study quasiconvex vector optimization with data uncertainty via robust optimization. By using scalarization, we introduce two types of surrogate duality theorems for robust quasiconvex vector optimization. We show surrogate min-max duality theorems for quasiconvex vector optimization with uncertain objective and/or constraints. For the problem with uncertain objective, we introduce its robust counterpart as a set-valued optimization problem.
robust vector optimization
sur- rogate duality
Applied Analysis and Optimization
Interdisciplinary Graduate School of Science and Engineering